Liver segmentation from low contrast open MR scans using k-means clustering and graph-cuts

Authors:
Yen-Wei Chen;Katsumi Tsubokawa;Amir H. Foruzan
Affiliations:
Electronics & Inf Eng School, Central South Univ of Forestry and Tech., China;College of Information Science and Eng., Ritsumeikan University, Shiga, Japan;College of Information Science and Eng., Ritsumeikan University, Shiga, Japan
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Year:
2010

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Abstract

Recently a growing interest has been seen in minimally invasive treatments with open configuration magnetic resonance (Open-MR) scanners Because of the lower magnetic field (0.5T), the contrast of Open-MR images is very low In this paper, we address the problem of liver segmentation from low-contrast Open-MR images The proposed segmentation method consists of two steps In the first step, we use K-means clustering and a priori knowledge to find and identify liver and non-liver index pixels, which are used as “object” and “background” seeds, respectively, for graph-cut In the second step, a graph-cut based method is used to segment the liver from the low-contrast Open MR images The main contribution of this paper is that the object (liver) and background (non-liver) seeds (regions) in every low-contrast slice of the volume can be obtained automatically by K-means clustering without user interaction.